NVIDIA Nemotron v3
Collection
Open, Production-ready Enterprise Models • 12 items • Updated
• 175
Stefan Schweter PRO
stefan-it
AI & ML interests
Flair Library 💕, NER & PoS Tagging, LM Pretraining (mostly encoder-only & encoder-decoder), Historical Language Models, German Language Models, Bavarian NLP 🥨
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upvoted a collection about 14 hours ago
NVIDIA Nemotron v3 liked
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Viet-Mistral/CulturaY upvoted a collection 1 day ago
MixtureVitae study models and datasets Organizations
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Article
Scaling Pedagogical Pre-training: From Optimal Mixing to 10 Billion Tokens
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reacted to hannayukhymenko's post with 🔥❤️ 10 days ago
Post
1912
Do you translate your benchmarks from English correctly? 🤔
Turns out, for many languages it is much harder than you can imagine!
Introducing Recovered in Translation 🌍 together with @aalexandrov
https://ritranslation.insait.ai
Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!
Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.
We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️
We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗
Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
Turns out, for many languages it is much harder than you can imagine!
Introducing Recovered in Translation 🌍 together with @aalexandrov
https://ritranslation.insait.ai
Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!
Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.
We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️
We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗
Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
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Post
4100
Do Bubbles Form When Tens of Thousands of AIs Simulate Capitalism?
We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena — tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.
Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump
NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).
Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding — this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.
Individual intelligence does not guarantee collective intelligence.
Dataset & Paper:
FINAL-Bench/Metacognitive
We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena — tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.
Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump
NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).
Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding — this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.
Individual intelligence does not guarantee collective intelligence.
Dataset & Paper:
FINAL-Bench/Metacognitive
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liked a dataset 15 days ago
upvoted a paper 15 days ago
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Article
Do Bubbles Form When Tens of Thousands of AIs Simulate Capitalism?
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17